Construction Of Emulators Research Articles

Novel statistical emulator construction for volcanic ash transport model Ash3d with physically motivated measures.

Statistical emulators are a key tool for rapidly producing probabilistic hazard analysis of geophysical processes. Given output data computed for a relatively small number of parameter inputs, an emulator interpolates the data, providing the expected value of the output at untried inputs and an estimate of error at that point. In this work, we propose to fit Gaussian Process emulators to the output from a volcanic ash transport model, Ash3d. Our goal is to predict the simulated volcanic ash thickness from Ash3d at a location of interest using the emulator. Our approach is motivated by two challenges to fitting emulators—characterizing the input wind field and interactions between that wind field and variable grain sizes. We resolve these challenges by using physical knowledge on tephra dispersal. We propose new physically motivated variables as inputs and use normalized output as the response for fitting the emulator. Subsetting based on the initial conditions is also critical in our emulator construction. Simulation studies characterize the accuracy and efficiency of our emulator construction and also reveal its current limitations. Our work represents the first emulator construction for volcanic ash transport models with considerations of the simulated physical process.

Active emulation of computer codes with Gaussian processes – Application to remote sensing

Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest. Nevertheless, the high cost involved hamper reliable and exhaustive simulations. Very often such codes incorporate heuristics that ironically make them less tractable and transparent. This paper introduces an active learning methodology for adaptively constructing surrogate models, i.e. emulators, of such costly computer codes in a multi-output setting. The proposed technique is sequential and adaptive, and is based on the optimization of a suitable acquisition function. It aims to achieve accurate approximations, model tractability, as well as compact and expressive simulated datasets. In order to achieve this, the proposed Active Multi-Output Gaussian Process Emulator (AMOGAPE) combines the predictive capacity of Gaussian Processes (GPs) with the design of an acquisition function that favors sampling in low density and fluctuating regions of the approximation functions. Comparing different acquisition functions, we illustrate the promising performance of the method for the construction of emulators with toy examples, as well as for a widely used remote sensing transfer code.

Black Box Attack of Simulation Secret Key using Neural-Identifier

The issue of the cryptanalysis is defined as the unknown issue or problem related to the identification of the system where the major goal of the cryptanalysis is for the design of the system for various steps involved. Neural networks will be ideal tool for Black-Box system identification. The black-Box attacks against secret key cryptosystems (stream cipher system) would be presented by considering a Black-Box Neuro-Identifier model to retain two different objectives: first is for finding out the key from the provided plaintext-cipher to pair, while the second objective is to emulator construction a neuro-model for the target cipher system. There are many researches going on considering the various models of encryption where ANN is being used as single layered or multi layered perceptron. The above defined cryptographic techniques are sometimes also termed as the Neural Cryptography. As the ANN model relies on the feedforward working criteria means it can be used for the generation of some effective and efficient encryption methodologies. Cryptanalysis is considered as significant footstep for evaluating and checking quality of any cryptosystem. A portion of these cryptosystem guarantees secrecy and security of huge data trade from source to goal utilizing symmetric key cryptography. The cryptanalyst researches the quality and distinguishes the shortcoming of the key just as enciphering calculation. With the expansion in key size, the time and exertion required anticipating the right key increments. These systems for cryptanalysis are changing radically to decrease cryptographic multifaceted nature. In this paper a point by point study has been directed. Much cryptography strategies are accessible which depend on number hypothesis however it has the hindrance of necessity a substantial computational power, unpredictability and time utilization. To defeat these disadvantages, artificial neural networks (ANNs) have been connected to take care of numerous issues. The ANNs have numerous qualities, for example, learning, speculation, less information necessity, quick calculation, simplicity of usage, and programming and equipment accessibility, which make it exceptionally alluring for some applications. This paper gives a cutting-edge survey on the utilization of counterfeit neural systems in cryptography and concentrates their execution on estimation issues identified with cryptography.

Euclid preparation: II. The EuclidEmulator – a tool to compute the cosmology dependence of the nonlinear matter power spectrum

We present a new power spectrum emulator named EuclidEmulator that estimates the nonlinear correction to the linear dark matter power spectrum. It is based on a spectral decomposition method called polynomial chaos expansion. All steps in the construction of the emulator have been tested and optimized: the large high-resolution N-body simulations carried out with PKDGRAV3 were validated using a simulation from the Euclid Flagship campaign and demonstrated to have converged up to wavenumbers $k\approx 5\,h\,{\rm Mpc}^{-1}$ for redshifts $z\leq 5$. The emulator is constructed using the uncertainty quantification software UQLab and it has been optimized first by creating mock emulators based on Takahashi's HALOFIT. We show that it is possible to successfully predict the performance of the final emulator in this way prior to performing any N-body simulations. We provide a C-code to calculate the nonlinear correction at a relative accuracy of $\sim0.3\%$ with respect to N-body simulations within 50 ms. The absolute accuracy of the final nonlinear power spectrum is comparable to one obtained with N-body simulations, i.e. $\sim 1\%$ for $k\lesssim 1\,h\,{\rm Mpc}^{-1}$ and $z\lesssim 3.5$. This enables efficient forward modeling in the nonlinear regime allowing for maximum likelihood estimation of cosmological parameters. EuclidEmulator has been compared to HALOFIT and CosmicEmu, an alternative emulator based on the Mira-Titan Universe, and shown to be more accurate than these other approaches. This work paves a new way for optimal construction of future emulators that also consider other cosmological observables, use higher resolution input simulations and investigate higher dimensional cosmological parameter spaces.

Automating Emulator Construction for Geophysical Hazard Maps

This paper describes an efficient and systematic process for using geophysical computer model simulations to guide efforts in probabilistic hazard mapping. The framework being proposed requires the simultaneous construction of many ($10^2$--$10^4$) statistical emulators, e.g., cheap model surrogates. This paper describes an automation process for choosing designs for and fitting these emulators. Throughout the description of this process, several useful modifications to standard emulators are explored. Additionally, this approach enables a fast and flexible uncertainty quantification for multiple sources of aleatory variability (natural randomness) and epistemic uncertainty (uncertainty in geophysical and statistical models) in the context of probabilistic hazard mapping. This process is illustrated through an application to granular volcanic flows.

Emulator-assisted reduced-rank ecological data assimilation for nonlinear multivariate dynamical spatio-temporal processes

As ecological data sets increase in spatial and temporal extent with the advent of new remote sensing platforms and long-term monitoring networks, there is increasing interest in forecasting ecological processes. Such forecasts require realistic initial conditions over complete spatial domains. Typically, data sources are incomplete in space, and the processes include complicated dynamical interactions across physical and biological variables. This suggests that data assimilation, whereby observations are fused with mechanistic models, is the most appropriate means of generating complete initial conditions. Often, the mechanistic models used for these procedures are very expensive computationally. We demonstrate a rank-reduced approach for ecological data assimilation whereby the mechanistic model is based on a statistical emulator. Critically, the rank-reduction and emulator construction are linked and, by utilizing a hierarchical framework, uncertainty associated with the dynamical emulator can be accounted for. This provides a so-called “weak-constraint” data assimilation procedure. This approach is demonstrated on a high-dimensional multivariate coupled biogeochemical ocean process.